Periodicity optimization in an automated tracing system

ABSTRACT

Periodicity similarity between two different tracer objectives may be used to identify additional input parameters to sample. The tracer objectives may be individual portions of a large tracer operation, and each of the tracer objectives may have separate set of input objects for which data may be collected. After collecting data for a tracer objective, other tracer objectives with similar periodicities may be identified. The input objects from the other tracer objectives may be added to a tracer objective and the tracer objective may be executed to determine a statistical significance of the newly added objective. An iterative process may traverse multiple input objects until exhausting possible input objects and a statistically significant set of input objects are identified.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application claims the benefit of priority to Non-Provisionalpatent application Ser. No. 13/765,654; filed Feb. 12, 2013; titledPERIODICITY OPTIMIZATION IN AN AUTOMATED TRACING SYSTEM; and naminginventors Russell S. KRAJEC and Ying LI. The above-cited application ishereby incorporated by reference, in its entirety, for all purposes.

BACKGROUND

Tracing gathers information about how an application executes within acomputer system. Tracing data may include any type of data that mayexplain how the application operates, and such data may be analyzed by adeveloper during debugging or optimization of the application. Tracingdata may also be used by an administrator during production operation ofthe application to identify various problems.

Tracing that occurs during development and debugging can be verydetailed. In some cases, the tracing operations may adversely affectsystem performance, as the tracing operations may consume large amountsof processing, storage, or network bandwidth.

SUMMARY

A tracing system may divide trace objectives across multiple instancesof an application, then deploy the objectives to be traced. The resultsof the various objectives may be aggregated into a detailed tracingrepresentation of the application. The trace objectives may definespecific functions, processes, memory objects, events, input parameters,or other subsets of tracing data that may be collected. The objectivesmay be deployed on separate instances of an application that may berunning on different devices. In some cases, the objectives may bedeployed at different time intervals. The trace objectives may belightweight, relatively non-intrusive tracing workloads that, whenresults are aggregated, may provide a holistic view of an application'sperformance.

A tracing system may perform cost analysis to identify burdensome orcostly trace objectives. For a burdensome objective, two or moreobjectives may be created that can be executed independently. The costanalysis may include processing, storage, and network performancefactors, which may be budgeted to collect data without undue performanceor financial drains on the application under test. A larger objectivemay be recursively analyzed to break the larger objective into smallerobjectives which may be independently deployed.

A tracing management system may use cost analyses and performancebudgets to dispatch tracing objectives to instrumented systems that maycollect trace data while running an application. The tracing managementsystem may analyze individual tracing workloads for processing, storage,and network performance costs, and select workloads to deploy based on aresource budget that may be set for a particular device. In some cases,complementary tracing objectives may be selected that maximizeconsumption of resources within an allocated budget. The budgets mayallocate certain resources for tracing, which may be a mechanism tolimit any adverse effects from tracing when running an application.

A tracing system may optimize collected data by identifyingperiodicities within the collected data, then updating sampling ratesand data collection windows. The updated parameters may be used tore-sample the data and perform more detailed analysis. The optimizationmay be based on a preliminary trace analysis from which a set offrequencies may be extracted as used for a default set of parameters.The tracing system may use multiple independent trace objectives thatmay be deployed to gather data, and each trace objective may beoptimized using periodicity analysis to collect statisticallysignificant data.

Periodicity similarity between two different tracer objectives may beused to identify additional input parameters to sample. The tracerobjectives may be individual portions of a large tracer operation, andeach of the tracer objectives may have separate set of input objects forwhich data may be collected. After collecting data for a tracerobjective, other tracer objectives with similar periodicities may beidentified. The input objects from the other tracer objectives may beadded to a tracer objective and the tracer objective may be executed todetermine a statistical significance of the newly added objective. Aniterative process may traverse multiple input objects until exhaustingpossible input objects and a statistically significant set of inputobjects are identified.

Tracer objectives in a distributed tracing system may be compared toidentify input parameters that may have a high statistical relevancy. Aniterative process may traverse multiple input objects by comparingresults of multiple tracer objectives and scoring possible input objectsas being possibly statistically relevant. With each iteration,statistically irrelevant input objects may be discarded from a tracerobjective and other potentially relevant objects may be added. Theiterative process may converge on a set of statistically relevant inputobjects for a given measured value without a priori knowledge of anapplication being traced.

A distributed tracing system may use independent tracer objectives forwhich a profile model may be created. The profile model may be deployedas a monitoring agent on non-instrumented devices to evaluate theprofile models. As the profile models operate with statisticallysignificant results, the sampling frequencies may be adjusted. Theprofile models may be deployed as a verification mechanism for testingmodels created in a more highly instrumented environment, and may gatherperformance related results that may not have been as accurate using theinstrumented environment. In some cases, the profile models may bedistributed over large numbers of devices to verify models based on datacollected from a single or small number of instrumented devices.

This Summary is provided to introduce a selection of concepts in asimplified form that are further described below in the DetailedDescription. This Summary is not intended to identify key features oressential features of the claimed subject matter, nor is it intended tobe used to limit the scope of the claimed subject matter.

BRIEF DESCRIPTION OF THE DRAWINGS

In the drawings,

FIG. 1 is a diagram illustration of an embodiment showing a system fortracing an application.

FIG. 2 is a diagram illustration of an embodiment showing a device thatmay create trace objectives, deploy the objectives, and analyze results.

FIG. 3 is a flowchart illustration of an embodiment showing a method forcreating and deploying objectives.

FIG. 4 is a flowchart illustration of an embodiment showing a method fordetermining a default sampling rate and data collection window.

FIG. 5 is a diagram illustration of an embodiment showing tracing withtracer objectives.

FIG. 6 is a flowchart illustration of an embodiment showing a method forcreating and deploying trace objectives.

FIG. 7 is a flowchart illustration of an embodiment showing a method forsizing tracer objectives using cost analysis.

FIG. 8 is a flowchart illustration of an embodiment showing a method fordividing tracer objectives using cost analysis.

FIG. 9 is a diagram illustration of an embodiment showing a process forfine tuning sampling rates and data collection windows.

FIG. 10 is a flowchart illustration of an embodiment showing a methodwith a feedback loop for evaluating tracer results.

FIG. 11 is a flowchart illustration of an embodiment showing a methodfor iterating on objectives using frequency similarity.

FIG. 12 is a diagram illustration of an embodiment showing a method forvalidating predictive models.

FIG. 13 is a flowchart illustration of an embodiment showing a methodfor analyzing results from tracer objectives.

FIG. 14 is a diagram illustration of an embodiment showing anenvironment with a tracing objective dispatcher.

FIG. 15 is a flowchart illustration of an embodiment showing a methodfor deploying tracer objectives.

FIG. 16 is a flowchart illustration of an embodiment showing a detailedmethod for tracer objective characterization and deployment.

DETAILED DESCRIPTION

Application Tracing with Distributed Objectives

A system for tracing an application may gather trace data from discrete,independent objectives that may be executed against multiple instancesof the application. The system may divide the tracing workload intoindividual objectives, then dispatch those objectives to collect subsetsof data. The trace data may be aggregated into a complete dataset.

In tracing a large application, the application may be considered to bea large system that responds to stimuli, which are the input events,data, or other stimuli. When a theoretical assumption may be made thatthe application behaves in a relatively consistent manner, the tracingmay be broken into many smaller units and the results aggregatedtogether to give a detailed picture of the entire application. Thesmaller units may be known as ‘trace objectives’ that may be dispatchedto gather some portion of the larger set of trace data.

The trace objectives may be a set of definitions for how to collecttrace data and conditions for collecting trace data. The traceobjectives may be consumed by a tracer operating within an instrumentedenvironment, which may be configured to collect many different types oftrace data and many different data objects. The objectives may alsoinclude connection definitions that establish a network connection to adata gathering and storage system. In many cases, the trace objectivesmay be described in a configuration file that may be transmitted to atracer.

In many cases, detailed tracing may consume a large amount of computing,storage, and network bandwidth resources. For example, many tracingalgorithms may increase the computation workload of a device by a factorof three or more. When such a load may be placed on a system, theperformance of the application may be extremely degraded. By creatingmany smaller objectives that each cause a small amount of tracing to beperformed, the detailed tracing results may still be achievable, butwith a lower impact to the running application.

A distributed tracing system may have a smaller footprint than a moredetailed tracing system, as the tracing workload may be distributed tomultiple instances of the application or as individual workloads thatmay be executed sequentially on one device. In many cases, the tracingmay be performed using a very large number of devices, where each deviceperforms a relatively small subset of the larger tracing task. In suchcases, a full view of the application functions may be obtained withminimal impact on each of the many devices.

The tracing system may automatically determine how to perform tracing inan optimized manner. An initial analysis of an application may uncovervarious functions, memory objects, events, or other objects that mayserve as the foundation for a trace objective. The automated analysismay identify related memory objects, functions, and various items forwhich data may be collected, all of which may be added to a traceobjective.

Once the trace objectives have been prepared, the trace objectives maybe dispatched to be fulfilled by various instrumented executionenvironments. The trace results may be transmitted to a centralizedcollector, which may store the raw data. For each objective, a postcollection analysis may evaluate the results to determine if the dataare sufficient to generate a meaningful summary statistic, which may bea profile model for how an application's various components respond toinput.

When the results of an objective cannot be verified with statisticalcertainty, the objective may be refactored and re-executed against theapplication. In some cases, the objective may be run for a longer timewindow to collect more data, while in other cases the objective may haveitems added or removed prior to re-execution.

Cost Analysis for Selecting Trace Objectives

A trace objective may be automatically evaluated using a cost analysisto determine if the objective may be too large or too burdensome toexecute. When the objective becomes too burdensome, the objective may besplit into two or more smaller objectives, where the results may becombined.

The cost analysis may evaluate execution costs, such as processorconsumption, network bandwidth consumption, storage consumption, powerconsumption, or other resource consumption. In many such cases, a costlimit may be placed on a trace objective to limit the amount ofresources that may be allocated for tracing. In some embodiments, thecost may be quantifiable financial costs that may be attributed toconsuming various resources.

Dividing a larger objective into multiple smaller objectives may userelationships within the various data objects to place related objectsin the same smaller objective. For example, a larger objective mayinvolve tracing multiple data items for an executable function. Some ofthe outputs of the function may be consumed by one downstream functionwhile other outputs of the function may be consumed by a differentdownstream function. When such relationships are available and known,the system may place the outputs for the first function in one traceobjective and the outputs for the second function in a second traceobjective.

The costs for analyzing an objective's impact may be estimated ormeasured. In some cases, an objective may be selected from a library ofdata collection templates. Each template may have estimated costs forperforming different aspects of the template, and the estimated costsmay be used for evaluating a trace objective.

In some cases, the costs for an objective may be measured. In suchcases, the objective may be executed for a short period of time whilecollecting cost data, such as impact on processors, storage, or networkbandwidth. Once such costs are known, an analysis may be performed todetermine whether or not to split the objective into multiple smallerobjectives.

Throughout this specification and claims, the term “costs” in thecontext of evaluating trace objectives may be a general term thatreflects any cost, expense, resource, tax, or other impediment createdby a trace objective. In general, costs refer to anything that has aneffect that may be minimized

Deploying Trace Objectives Using Cost Analyses

Trace objectives may be deployed using cost estimate for the traceobjectives and resource budgets on tracing devices. The budgets maydefine a resource allocation for trace objectives, and a dispatcher mayselect trace objectives that may utilize the allocated resources.

Multiple trace objectives may be dispatched to a device when the sum ofthe resources consumed by all of the trace objectives are less than thebudgeted amount. The trace objectives may be dispatched using a manifestthat may include all of the assigned trace objectives.

A trace resource budget may define a maximum amount of resources thatmay be allocated to tracing workloads on a particular device. The budgetmay vary between devices, based on the hardware and softwareconfiguration, as well as any predefined resource or performanceallocations. In some cases, a particular device or instance of anapplication may be allocated to meet minimum performance standards,leaving remaining resources to be allocated to tracing operations.

The assignment of trace objectives by cost may allow a minimumapplication performance to be maintained even while tracing is beingperformed. The minimum application performance may ensure thatapplication throughput may be maintained when tracing is deployed in aproduction environment, as well as ensure that tracing does notadversely affect any data collected during tracing.

Periodicity Optimization in an Automated Tracing System

An automated tracing system may analyze periodicities in collected data,then adjust sampling rates and data collection windows to collect datathat effectively captures the observed periodicities. An initial, highlevel trace may gather general performance parameters for an arbitraryapplication under test.

From the initial tracing, periodicity analysis may be performed toidentify characteristic frequencies of the data. The characteristicfrequencies of the initial data may be used to set a default samplingrate and data collection window for detailed tracer objectives that maybe deployed.

As results may be captured from the tracer objectives, a secondperiodicity analysis may identify additional repeating patterns in thedata. From the second periodicity analysis, the sampling rate and datacollection window may be updated or optimized to collect statisticallymeaningful data.

In some embodiments, a tracer objective may be deployed with differentparameters to explore repeating patterns at higher or lower frequenciesthan the default settings. Such an embodiment may test for statisticallyrelevant frequencies, then collect additional data when statisticallyrelevant frequencies are found. As an arbitrary application is traced,the list of dominant frequencies within the application may be appliedto other tracer objectives.

The sampling rate of a tracer objective may define the smallest periodor highest frequency that may be observed in a time series of data.Similarly, the data collection window may define the largest period orlowest frequency that may be observed. By ensuring that knownfrequencies are covered in a results set, a statistically meaningfuldetermination may be made whether or not such frequencies appear in aset of observed data.

Optimization Analysis Using Similar Frequencies

An automatic optimization system may create statistically meaningfulrepresentations of an application performance by iterating on the inputparameters that may affect a traced performance metric. After selectinga starting set of potential input parameters that may affect a measuredor traced metric, statistically insignificant input parameters may beremoved and potentially relevant parameters may be added to a tracerobjective.

The observed metric may be analyzed for periodicity, the result of whichmay be a set of frequencies found in the data. The set of frequenciesmay be used as a signature, which may be matched with frequencysignatures of other tracer objectives. The matching tracer objectivesmay be analyzed to identify statistically significant input parametersin the other tracer objectives, and those input parameters may beconsidered as potential input parameters.

The frequency analysis may attempt to match tracer objectives that havesimilar observed characteristics in the time domain by matching similarfrequency signatures. Two tracer objectives that may have similarfrequency signatures may react similarly to stimuli or have otherbehavioral similarities. In many cases, the input parameters that mayaffect the behavior observed with one tracer objective may be somehowrelated to input parameters that may affect the behavior observed withanother tracer objective.

In some cases, the frequency comparisons may examine a dominantfrequency found within the data. Such cases may be occur when analysisof the various tracer objective results yields several differentdominant frequencies. In other cases, a single dominant frequency may beobserved in a large number of results sets. In such cases, thecomparisons may be made using a secondary frequency which may be acharacteristic frequency after the dominant frequency may be removed.

In embodiments where multiple frequencies may be observed from the data,a frequency signature may be created that reflects the frequencies andthe strength or importance of each frequency. The signatures may becompared using a similarity comparison to identify matches. In someembodiments, the comparisons may be performed using a score that mayindicate a degree of similarity.

Deployment of Profile Models with a Monitoring Agent

Some tracing systems may create profile models that may representtracing data. The models may then be deployed to monitors that may testthe profile models against additional data. When the profile modelssuccessfully track additional data, the monitoring may be halted orreduced to a lower frequency. When the profile models may notsuccessfully track additional data, the trace objectives used to createthe original data may be refactored and redeployed so that new orupdated models may be generated.

The monitoring system may operate with less cost than with a tracer. Inmany cases, a tracer may consume overhead processes, storage, andnetwork traffic that may adversely affect application performance andmay adversely affect financial costs of executing an application. Amonitoring system may have much less overhead than a tracer and may beconfigurable to gather just specific data items and test the data itemsusing a profile model.

In some systems, an instrumented execution environment with a tracersystem may be deployed on a subset of devices, while a monitoring systemmay be deployed on all or a larger subset of devices. By using themonitoring system for testing or verification of the profile models, thecomplex and costly data collection operations may be performed on asubset of devices while the less costly monitoring operations may beperformed on a different subset of devices.

Throughout this specification and claims, the term “trace objective” or“tracer objective” is used to refer to a set of configuration settings,parameters, or other information that may be consumed by a tracer tocollect data while an application executes. The trace objective may beembodied in any manner, such as a configuration file or other definitionthat may be transmitted to and consumed by a tracer. In some cases, thetrace objective may include executable code that may be executed by thetracer in order to collect data. The tracer object may often contain aconnection definition that may enable a network connection to a remotedevice that may collect data for storage and analysis.

Throughout this specification and claims, the terms “profiler”,“tracer”, and “instrumentation” are used interchangeably. These termsrefer to any mechanism that may collect data when an application isexecuted. In a classic definition, “instrumentation” may refer to stubs,hooks, or other data collection mechanisms that may be inserted intoexecutable code and thereby change the executable code, whereas“profiler” or “tracer” may classically refer to data collectionmechanisms that may not change the executable code. The use of any ofthese terms and their derivatives may implicate or imply the other. Forexample, data collection using a “tracer” may be performed usingnon-contact data collection in the classic sense of a “tracer” as wellas data collection using the classic definition of “instrumentation”where the executable code may be changed. Similarly, data collectedthrough “instrumentation” may include data collection using non-contactdata collection mechanisms.

Further, data collected through “profiling”, “tracing”, and“instrumentation” may include any type of data that may be collected,including performance related data such as processing times, throughput,performance counters, and the like. The collected data may includefunction names, parameters passed, memory object names and contents,messages passed, message contents, registry settings, register contents,error flags, interrupts, or any other parameter or other collectabledata regarding an application being traced.

Throughout this specification and claims, the term “executionenvironment” may be used to refer to any type of supporting softwareused to execute an application. An example of an execution environmentis an operating system. In some illustrations, an “executionenvironment” may be shown separately from an operating system. This maybe to illustrate a virtual machine, such as a process virtual machine,that provides various support functions for an application. In otherembodiments, a virtual machine may be a system virtual machine that mayinclude its own internal operating system and may simulate an entirecomputer system. Throughout this specification and claims, the term“execution environment” includes operating systems and other systemsthat may or may not have readily identifiable “virtual machines” orother supporting software.

Throughout this specification, like reference numbers signify the sameelements throughout the description of the figures.

When elements are referred to as being “connected” or “coupled,” theelements can be directly connected or coupled together or one or moreintervening elements may also be present. In contrast, when elements arereferred to as being “directly connected” or “directly coupled,” thereare no intervening elements present.

The subject matter may be embodied as devices, systems, methods, and/orcomputer program products. Accordingly, some or all of the subjectmatter may be embodied in hardware and/or in software (includingfirmware, resident software, micro-code, state machines, gate arrays,etc.) Furthermore, the subject matter may take the form of a computerprogram product on a computer-usable or computer-readable storage mediumhaving computer-usable or computer-readable program code embodied in themedium for use by or in connection with an instruction execution system.In the context of this document, a computer-usable or computer-readablemedium may be any medium that can contain, store, communicate,propagate, or transport the program for use by or in connection with theinstruction execution system, apparatus, or device.

The computer-usable or computer-readable medium may be, for example butnot limited to, an electronic, magnetic, optical, electromagnetic,infrared, or semiconductor system, apparatus, device, or propagationmedium. By way of example, and not limitation, computer readable mediamay comprise computer storage media and communication media.

Computer storage media includes volatile and nonvolatile, removable andnon-removable media implemented in any method or technology for storageof information such as computer readable instructions, data structures,program modules or other data. Computer storage media includes, but isnot limited to, RAM, ROM, EEPROM, flash memory or other memorytechnology, CD-ROM, digital versatile disks (DVD) or other opticalstorage, magnetic cassettes, magnetic tape, magnetic disk storage orother magnetic storage devices, or any other medium which can be used tostore the desired information and which can accessed by an instructionexecution system. Note that the computer-usable or computer-readablemedium could be paper or another suitable medium upon which the programis printed, as the program can be electronically captured, via, forinstance, optical scanning of the paper or other medium, then compiled,interpreted, of otherwise processed in a suitable manner, if necessary,and then stored in a computer memory.

When the subject matter is embodied in the general context ofcomputer-executable instructions, the embodiment may comprise programmodules, executed by one or more systems, computers, or other devices.Generally, program modules include routines, programs, objects,components, data structures, etc. that perform particular tasks orimplement particular abstract data types. Typically, the functionalityof the program modules may be combined or distributed as desired invarious embodiments.

FIG. 1 is a diagram of an embodiment 100 showing a system for tracing anapplication. Embodiment 100 is a simplified example of a sequence forcreating trace objectives, deploying the objectives, and analyzing theresults.

Embodiment 100 illustrates an example of a tracing system that may befully automated or at least largely automated to collect data about anapplication. The resulting data may be a characterization of theapplication, including profile models of the application as a whole orat least for some subsets of the application. The results may be used toanalyze and debug the application, design monitoring metrics, or otheruses.

Embodiment 100 illustrates a generalized operation that takes anapplication 102 and does some preliminary analysis 104 to create lists106 of events, functions, memory objects, and other potentiallyinteresting objects for tracing. From the lists 106, instrumentation ortrace objectives 108 may be created and deployed 110 to variousinstrumented devices 112, 114, and 116.

Each of the instrumented devices 112, 114, and 116 may execute aninstance of the application 118, 120, and 122, respectively, and theinstrumentation may generate results in the form of input streams andtracer results 124. The results 124 may be analyzed 126, which may causethe instrumentation objectives 108 to be updated and redeployed, or anaggregated results set 128 may be generated.

The various instrumented devices may be any device capable of collectingdata according to a trace objective. In some cases, the instrumenteddevices may have specialized or dedicated hardware or softwarecomponents that may collect data. In other cases, an instrumented systemmay be a generic system that may be configured to collect data asdefined in a tracer objective.

Embodiment 100 illustrates a system that may be automated to generatetracing data for an application by splitting the tracing workload intomany small trace objectives. The smaller trace objectives may bedeployed such that the trace objectives may not adversely interfere withthe execution of the application.

Smaller trace objectives may allow much more detailed and fine graineddata collection than may be possible with a complete tracer that maycapture all data at once. In many cases, capturing a very detailed setof data may consume large amounts of processor, storage, networkbandwidth, or other resources.

When smaller trace objectives are used, the data collected fromdifferent trace objectives may not be from precisely the same set ofinput parameters to the application. As such, the results from thesmaller trace objectives may undergo various analyses to determinewhether or not the results may be repeatable. When the results are shownto be repeatable, the results may be aggregated from multiple traceobjectives to create a superset of data.

Embodiment 100 illustrates an example where an application may beperformed by several devices. In some cases, each device may execute anidentical instance of the application. An example may be a websiteapplication that may be load balanced such that each device executes anidentical copy. In other cases, each device may execute a subset of alarger application. An example may be a distributed application whereeach device performs a set of functions or operations that may causedata to pass to another device for further processing.

FIG. 2 is a diagram of an embodiment 200 showing a computer system witha system for automatically tracing an application using independenttrace objectives. Embodiment 200 illustrates hardware components thatmay deliver the operations described in embodiment 100, as well as otherembodiments.

The diagram of FIG. 2 illustrates functional components of a system. Insome cases, the component may be a hardware component, a softwarecomponent, or a combination of hardware and software. Some of thecomponents may be application level software, while other components maybe execution environment level components. In some cases, the connectionof one component to another may be a close connection where two or morecomponents are operating on a single hardware platform. In other cases,the connections may be made over network connections spanning longdistances. Each embodiment may use different hardware, software, andinterconnection architectures to achieve the functions described.

Embodiment 200 illustrates a device 202 that may have a hardwareplatform 204 and various software components. The device 202 asillustrated represents a conventional computing device, although otherembodiments may have different configurations, architectures, orcomponents.

In many embodiments, the optimization server 202 may be a servercomputer. In some embodiments, the optimization server 202 may stillalso be a desktop computer, laptop computer, netbook computer, tablet orslate computer, wireless handset, cellular telephone, game console orany other type of computing device.

The hardware platform 204 may include a processor 208, random accessmemory 210, and nonvolatile storage 212. The hardware platform 204 mayalso include a user interface 214 and network interface 216.

The random access memory 210 may be storage that contains data objectsand executable code that can be quickly accessed by the processors 208.In many embodiments, the random access memory 210 may have a high-speedbus connecting the memory 210 to the processors 208.

The nonvolatile storage 212 may be storage that persists after thedevice 202 is shut down. The nonvolatile storage 212 may be any type ofstorage device, including hard disk, solid state memory devices,magnetic tape, optical storage, or other type of storage. Thenonvolatile storage 212 may be read only or read/write capable. In someembodiments, the nonvolatile storage 212 may be cloud based, networkstorage, or other storage that may be accessed over a networkconnection.

The user interface 214 may be any type of hardware capable of displayingoutput and receiving input from a user. In many cases, the outputdisplay may be a graphical display monitor, although output devices mayinclude lights and other visual output, audio output, kinetic actuatoroutput, as well as other output devices. Conventional input devices mayinclude keyboards and pointing devices such as a mouse, stylus,trackball, or other pointing device. Other input devices may includevarious sensors, including biometric input devices, audio and videoinput devices, and other sensors.

The network interface 216 may be any type of connection to anothercomputer. In many embodiments, the network interface 216 may be a wiredEthernet connection. Other embodiments may include wired or wirelessconnections over various communication protocols.

The software components 206 may include an operating system 218 on whichvarious software components and services may operate. An operatingsystem may provide an abstraction layer between executing routines andthe hardware components 204, and may include various routines andfunctions that communicate directly with various hardware components.

Embodiment 200 illustrates many software components 206 as deployed on asingle device 202. In other embodiments, some or all of the varioussoftware components 206 may be deployed on separate devices or even onclusters of devices.

Device 202 illustrates many of the software components that may managethe tracing of an application 220.

A preliminary analysis of the application 220 may be performed using astatic code analyzer 222 or a high level tracer 224. In someembodiments, both a static code analyzer 222 and a high level tracer 224may be used.

The static code analyzer 222 may examine source code, intermediate code,binary code, or other representation of the application 220 to identifyvarious elements that may be traced or for which data may be collected.For example, a static code analyzer 222 may identify various functions,subroutines, program branches, library routines, or other portions ofthe executable code of the application 220, each of which may be anelement for which data may be gathered. Additionally, a static codeanalyzer 222 may identify memory objects, parameters, input objects,output objects, or other memory elements or data objects that may besampled or retrieved.

The high level tracer 224 may be a lightweight tracing system that maymonitor an executing application 220 and identify sections of code thatare executed, memory objects that are manipulated, interrupts that maybe triggered, errors, inputs, outputs, or other elements, each of whichmay or may not have data elements that may be gathered during tracing.

The static code analyzer 222 or the high level tracer 224 may create aflow control graph or other representation of relationships betweenelements. The relationships may be traversed to identify related objectsthat may be useful when generating trace objectives 228.

The various elements may be analyzed by the trace objective generator226 to create a trace objective 228. Once created, a dispatcher 230 maycause the trace objectives 228 to be executed by a tracer.

The trace objective generator 226 may generate independently executabletrace objectives that generate data regarding the application 220 whenthe application 220 is executed. The independent trace objectives 228may be constructed by identifying an element to be traced, which may bea function, memory object, interrupt, input object, output object, orother element.

Once a starting element may be identified, the trace objective generator226 may attempt to find related items that may also be traced. Forexample, a function may be identified as a starting element. Relateditems may include input parameters passed to the function and resultstransmitted from the function. Further related items may be functionscalled by the starting function and the various parameters passed tothose functions. Regarding each function, related items may include theprocessing time consumed by the function, heap memory allocated, memoryobjects created or changed by the function, and other parameters.

In some embodiments, a set of trace objective templates 227 may beavailable. A trace objective template 227 may be a starting frameworkfor tracing a specific object. For example, a trace objective template227 may be created for tracing a specific type of function, where thetemplate may include parameters that may typically be measured for aspecific type of function. Other examples may include templates fortracing different types of memory objects, interrupts, input objects,output objects, error conditions, and the like.

The various templates may include cost estimating parameters, which maybe used to assess or estimate the impact of a particular traceobjective. The cost estimating parameters may include financial cost aswell as performance costs, resource consumption costs, or other costs.The estimated costs may be a factor used by a trace objective generator226 to determine whether a given trace objective may be too large,complex, or costly to execute and therefore may be split into multiplesmaller trace objectives.

When a high level tracer 224 may be used, periodicity data may beextracted from the data collected. Periodicity data may include anyrepeating pattern or frequency of data that repeats. Periodicity datamay be used by the trace objective generator 226 to select a datacollection window that may be sized to capture periodic data. When adata collection window is smaller than a known repeating period, anyprofile model or other analysis may not fully capture the behavior ofthe data.

The trace objective generator 226 may create execution parameters for atrace objective. The execution parameters may include a data collectionwindow. In some cases, a data collection window may be defined by astart time and end time. In other cases, a data collection window may bedefined by a number of values collected, amount of data collected, orother conditions. In still other cases, starting and stopping conditionsmay include event monitoring. For example, a starting condition maybegin tracing when a specific input event occurs or an ending conditionmay be defined when a memory object reaches a certain value.

The execution parameters may include data collection parameters, such assampling frequency. In some cases, data collection parameters may alsoinclude definitions of when to collect data, which may be dependent oncalculated, measured, or observed data. For example, data may becollected when a parameter X is equal to zero, when the processor loadis less than 80%, or some other condition.

The trace objective generator 226 may transmit executable code to atracer. The executable code may include condition definitions or othercode that may be evaluated during execution. The executable code mayalso include instrumentation or other code that may collect specifictypes of data.

In some cases, the executable code may be inserted into an applicationto retrieve values, perform calculations, or other functions that maygenerate data. In some embodiments, executable code may be included intrace objective templates 227, and the executable code may be customizedor modified by the trace objective generator 226 prior to inclusion in atrace objective.

The trace objective generator 226 may define input conditions for agiven traced object. The input conditions may be data that are collectedin addition to the objects targeted for monitoring. In some embodiments,the input conditions may be analyzed and evaluated to compare differentruns of the same or related trace objectives. The input conditions mayinclude any input parameter, object, event, or other condition that mayaffect the monitored object. In many embodiments, a profile model may becreated that may represent the behavior of the monitored object, and theinput conditions may be used as part of the profile model.

The trace objective generator 226 may create multiple trace objectives228 which may be transmitted to various instrumented systems 246 by adispatcher 230.

The dispatcher 230 may determine a schedule for executing traceobjectives and cause the trace objectives to be executed. The schedulemay include identifying which device may receive a specific traceobjective, as well as when the trace objective may be executed. In somecases, the dispatcher 230 may cause certain trace objectives to beexecuted multiple times on multiple devices and, in some cases, inmultiple conditions.

A data collector 234 may receive output from the trace objectives andstore the results and input stream 236 in a database. An analyzer 232may analyze the data to first determine whether the data may berepeatable, then to aggregate results from multiple trace objectivesinto an aggregated results set 238. In many embodiments, the analyzer232 may create profile models that may represent the observed data. Suchprofile models may be used for various scenarios, such as identifyingbottlenecks or mapping process flow in a development or debuggingscenario, monitoring costs or performance in a runtime or administrativescenario, as well as other uses.

The instrumented systems 246 may be connected to the device 202 througha network 244. The network 244 may be the Internet, a local areanetwork, or any other type of communications network.

The instrumented systems 246 may operate on a hardware platform 248which may have an instrumented execution environment 252 on which anapplication 250 may execute. The instrumented execution environment 252may be an operating system, system virtual machine, process virtualmachine, or other software component that may execute the application250 and provide a tracer 254 or other instrumentation that may collectdata during execution.

The tracer 254 may receive trace objectives 256 from the dispatcher 230.The tracer 254 may evaluate and execute the trace objectives 256 tocollect input data and tracer results, then transmit the input data andtracer results to the data collector 234.

In some embodiments, a single tracer 254 may have multiple traceobjectives 256 that may be processed in parallel or at the same time. Insome such embodiments, a dispatcher 230 may identify two or more traceobjectives 256 that may not overlap each other. An example may include afirst trace objective that gathers data during one type of operation anda second trace objective that gathers data during another type ofoperation, where the two operations may not occur at the same time. Insuch an example, neither trace objective would be executing while theother tracer object were executing.

In another example, some trace objectives 256 may be very lightweight inthat the trace objective may not have much impact or cost on theinstrumented systems 246. In such cases, the dispatcher 230 may sendseveral such low cost or lightweight trace objectives 256 to theinstrumented systems 246.

In some embodiments, the trace objective generator 226 may create traceobjectives that may be sized to have minimal impact. Such traceobjectives may be created by estimating the cost impact on aninstrumented system 246. The cost impact may include processing,input/output bandwidth, storage, memory, or any other impact that atrace objective may cause.

The trace objective generator 226 may estimate the cost impact of aproposed trace objective, and then split the trace objective intosmaller, independent trace objectives when the cost may be above aspecific threshold. The smaller trace objectives may also be analyzedand split again if they may still exceed the threshold.

Such embodiments may include a cost analysis, performance impact, orother estimate with each trace objective. In such embodiments, adispatcher 230 may attempt to match trace objectives with differing costconstraints. For example, a dispatcher 230 may be able to launch onetrace objective with high processing costs with another trace objectivewith little processing costs but high storage costs. Both traceobjectives together may not exceed a budgeted or maximum amount ofresource consumption.

The analyzer 232 may create profile models of the tracer results andinput stream 236. The profile models may be a mathematical or otherexpression that may predict an object's behavior based on a given set ofinputs. Some embodiments may attempt to verify profile models byexercising the models with real input data over time to compare themodel results with actual results.

Some such embodiments may use a monitoring system to evaluate profilemodels. A monitoring manager 240 may dispatch the models to varioussystems with monitoring 256. The systems with monitoring 256 may have ahardware platform 258 on which an execution environment 260 may run anapplication 262. A monitor 264 may receive configurations 266 which mayinclude profile models to evaluate.

The monitor 264 may be a lightweight instrumentation system. In manycases, the systems with monitoring 256 may be production systems wherethe monitor 264 may be one component of a larger systems administrationand management system. The monitor 264 may evaluate a profile model togenerate an error statistic. The error statistic may represent thedifference between a predicted value and an actual value. When the errorstatistic is high, the profile model may be reevaluated by creating anew or updated trace objective. When the error statistic is low, theprofile model may be used to represent the observed data with a highdegree of confidence.

The architecture of embodiment 200 illustrates two different types ofsystems that may execute an application. The systems with monitoring 256may represent production systems on which an application may run, whilethe instrumented systems 246 may be specialized systems that may haveadditional data collection features. In some cases, the instrumentedsystems 246 may be the same or similar hardware as the systems withmonitoring 256, and may be specially configured. In still otherembodiments, the two types of systems may be identical in both hardwareand software but may be used in different manners.

In some embodiments, the various components that may generate tracingobjectives may also be deployed on the same device that may execute thetraced application and collect the results. In some such embodiments,some components may be allocated to certain processors or otherresources while other components may be allocated to differentresources. For example, a processor or group of processors may be usedfor executing and tracing an application, while other processors maycollect and analyze tracer results. In some cases, a tracer objectivemay execute on one processor and monitor the operations of anapplication executing on a different processor.

FIG. 3 is a flowchart illustration of an embodiment 300 showing a methodfor creating and deploying trace objectives. Embodiment 300 illustratesthe operations of a device 202 as illustrated in embodiment 200.

Other embodiments may use different sequencing, additional or fewersteps, and different nomenclature or terminology to accomplish similarfunctions. In some embodiments, various operations or set of operationsmay be performed in parallel with other operations, either in asynchronous or asynchronous manner. The steps selected here were chosento illustrate some principles of operations in a simplified form.

Embodiment 300 illustrates a general method by which trace objectivesmay be created and deployed. Some of the components of embodiment 300may be illustrated in more detail in other embodiments described laterin this specification.

Embodiment 300 illustrates a method whereby static code analysis and aninitial tracing operation may identify various objects for tracing. Insome embodiments, the initial tracing operation may identify enoughinformation from which tracing objectives may be created. In otherembodiments, an initial tracing operation may identify objects fortracing, then a second initial tracing operation may be performed foreach of the objects. The second initial tracing operation may collectdetailed data that may be too cumbersome or impractical to gather formany objects in a single tracing operation.

An application may be received in block 302 for evaluation. In block303, the application may undergo preliminary analysis. The preliminaryanalysis may gather various information that may be used toautomatically create a set of tracer objectives. The tracer objectivesmay be iterated upon to converge on statistically relevant inputparameters that may affect a monitored parameter. The preliminaryanalysis of block 303 may gather objects to monitor as well asoperational limits that may be used to create tracing objectives.

The preliminary analysis may also include periodicity analysis that maybe used to set sampling rates and data collection windows forobjectives. The sampling rates and data collection windows may beadjusted over time as additional data are collected and analyzed.

Static code analysis may be performed in block 304 to identify potentialtracing objects. Static code analysis may identify functions and otherexecutable code elements, memory objects and other storage elements, andother items.

In some embodiments, static code analysis may also generaterelationships between executable code elements and memory objects. Anexample of relationships may include flow control graphs that may showcausal or communication relationships between code elements. In manycases, memory objects may be related to various code elements.

High level tracing may be performed in block 306. High level tracing mayhelp identify objects for tracing as well as gather some high levelperformance or data characteristics that may be used later whengenerating trace objectives.

During execution with high level tracing, execution elements andexecution boundaries may be identified in block 308. The executionelements may be functions, libraries, routines, blocks of code, or anyother information relating to the executable code. Execution boundariesmay refer to performance characteristics such as amount of time toexecute the identified portions of the application, as well as theexpected ranges of values for various memory objects. The executionboundaries may include function calls and returns, process spawn events,and other execution boundaries.

Causal relationships may be identified between components in block 308.Causal relationships may be cause and effect relationships where oneobject, function, condition, or other input may cause a function tooperate, a memory object to change, or other effect. Causalrelationships may be useful in identifying or gathering related objectstogether for instrumentation.

Input parameters may be identified in block 310. The input parametersmay include any inputs to the application, including data passed to theapplication, input events, or other information that may cause behaviorsin the application. In some embodiments, the various execution elementsmay be analyzed to identify input parameters that may be directed tospecific execution elements.

The high level tracing may identify various memory objects that maychange during execution in block 312. The memory objects may representobjects for which a trace objective may be created, which may be addedto a list of possible objects for tracing in block 314.

While the high level tracing executes, any periodicities or repeatingpatterns may be identified in block 316. Many applications operate in arepeating fashion, and often have multiple periodicities. For example, aretail website application may have a seasonal periodicity where theworkload increases near holidays, as well as a weekly periodicity wherethe workload predictably varies over the day of week. The sameapplication may experience repeatable changes for the hour of the day aswell.

When the periodicities of an application may be known, the datacollection windows for a tracer object may be set to capture multiplecycles of a period. Data that captures multiple cycles may be used togenerate profile models that include a factor that takes into accountperiodicity. When the data collection window does not collect enoughdata to capture the periodicity, a profile model may generate moreerrors, making the model less reliable and repeatable.

Several performance tests may be performed, including storage tests inblock 318, network bandwidth in block 320, and available computationalbandwidth in block 322. The performance tests may be performed under thesame or similar conditions as the trace objectives may be run. Forexample, the performance tests of blocks 318, 320, and 322 may beexecuted on an instrumented system while the application is executing.

The performance tests may be used to set boundaries or thresholds forcreating trace objectives that meet a maximum cost goal. In suchembodiments, the performance tests may be analyzed to determine theremaining performance bandwidth while an application executes. For anapplication that may be compute bound, computational performance may beheavily used, but there may be excess storage and network bandwidth thatmay be consumed by trace objectives. In another example, an applicationmay be network or input/output bound, leaving excess computation freefor use by trace objectives.

In many cases, a budget or goal may be defined for the cost of tracing.For example, a goal may be set to use up to 10%, 20%, 50%, or some othervalue of system resources for tracing uses. When such a goal may be set,trace objectives may be created small enough and lightweight enough tomeet the goal, and the trace objectives may be dispatched or scheduledto meet the goal.

The allocation of tracing resources may be useful when an applicationperforms time sensitive operations, or when the tracing may be focusedon performance monitoring or optimization. By allocating only a maximumamount of resources, the application may not be adversely affected byexcessive tracing.

In block 324, trace objectives may be created. Examples of more detailedmethods for creating trace objectives are provided later in thisspecification. Deployment objectives may be created in block 326 togenerate a deployment schedule, and the objectives may be deployed inblock 328.

As the objectives are deployed, results may be received and analyzed inblock 330. The analysis may identify changes to be made to a traceobjective, such as changes to the sampling rate or data collectionwindow from periodicity analysis or changes to collecting certain inputdata streams. Such changes may cause the tracer objectives to be updatedin block 332 and redeployed at block 326.

FIG. 4 is a flowchart illustration of an embodiment 400 showing a methodfor determining a default sampling rate and data collection window.Embodiment 400 illustrates some operations of a device 202 asillustrated in embodiment 200.

Other embodiments may use different sequencing, additional or fewersteps, and different nomenclature or terminology to accomplish similarfunctions. In some embodiments, various operations or set of operationsmay be performed in parallel with other operations, either in asynchronous or asynchronous manner. The steps selected here were chosento illustrate some principles of operations in a simplified form.

Embodiment 400 illustrates a method for determining an initial set ofsettings for sampling rate and a data collection window for tracerobjectives. In general, a sampling rate for a time series may reflectthe highest frequency that may be observed in a data stream. As asampling rate becomes faster and the time slices of a data sample becomeshorter, the data may capture higher frequencies. As the sampling ratedecreases, the higher frequencies may not be detectable in the datastream and may add to observed noise.

A data collection window may define the longest frequency that may beobserved in a time series data set. In general, a statisticallysignificant sample size may be at least two or three times the period ofthe longest period within the data. A data collection window that issmaller than the longest period within the data may result in a data setthat contains observed noise.

The operations of embodiment 400 may be used to set an initial samplingrate and data collection window that may be applied as a default totracer objectives. Once the tracer objectives have been deployed andtheir resulting data analyzed, changes may be made to the sampling rateand data collection window.

Initial trace results may be received in block 402. The initial traceresults may come from a preliminary trace of an application. Thepreliminary trace may identify several parameters to measure and severalinput streams to capture. In many cases, the preliminary trace may beperformed with little or no knowledge of the application.

An autocorrelation analysis may be performed in block 404 to identifydominant periodicities in the data. The periodicity analysis of block404 may identify multiple frequencies that may be contained in the data.Some of the frequencies may have a stronger influence than otherfrequencies.

A long frequency may be identified in block 406 and may be used todetermine a default data collection window. A data collection window maydefine a length of time that time series samples may be taken. Ingeneral, a data collection window may be selected to be two, three, ormore times the length of the longest period or frequency.

A small periodicity may be identified in block 408 and used to determinea default sampling rate. The default sampling rate may be short enoughthat the smallest frequency may be captured by 5, 10, or more samples.

The default data collection window and sampling rate may be stored inblock 410. The default data collection window and sampling rate may beused as a starting point for a tracer objective. In many cases, the datacollection window and sampling rate may be adjusted after analyzing moredetailed data.

In some embodiments, a default sampling rate and data collection windowmay be set to be related to each other. For example, a default samplingrate may be set using a dominant frequency of initial data, then adefault data collection window may be set to be a predefined multiple ofdata samples. In one such example, a default data collection window maybe set to be 10,000 times the length of a default sampling window, whichmay result in 10,000 sets of time series data for analysis.

In another example, a default data collection window may be determinedby a relatively long dominant frequency, and a sampling rate may bedetermined to yield a predefined number of samples. In one such example,a default data collection window may be set to be an hour, and asampling rate may be set to be 0.36 seconds to yield 10,000 samples perrun.

FIG. 5 is a diagram illustration of an embodiment 500 showing a highlevel process for creating individual trace objectives then aggregatingthe collected data. The process of embodiment 500 creates independenttrace objectives that may be deployed and optimized using severaloptimization analyses. Once the trace objectives have converged onstatistically meaningful results, the results from multiple traceobjectives may be aggregated.

A set of initial trace objectives may be analyzed, improved, anditerated to converge on statistically meaningful results. Embodiment 500may represent an automated methodology for tracing an arbitraryapplication by using small, independent tracer objectives. The traceobjectives may be divided, split, or otherwise made small enough to meeta tracer budget, then the trace objectives may be independently run andevaluated.

An overall objective to collect trace data may be defined in block 502.A cost analysis may be performed in block 504 to determine if the traceobjective may be achieved. When the trace objective exceeds a set ofcost goals, the objective may be divided in block 506 into smallerobjectives, which may again be evaluated by the cost analysis in block504. The iterative process of blocks 504 and 506 may result in multipletrace objectives that meet a cost goal.

The cost goals may be a mechanism to create tracer objectives that maybe sized appropriately for a given application and a given scenario. Bysizing a tracer objective so that the tracer objective does not exceed acost goal, any negative influence of the tracer objective may beminimized during data collection.

Several different tracing scenarios may be supported. In one scenario,an application may be deployed on a large number of devices. One examplemay be a website that may be deployed on several servers in adatacenter, where all of the servers operate as a cluster to handleincoming web requests in parallel. In such an example, the performanceof the servers may be more accurately measured when the tracerobjectives are relatively small and consume few resources.

In another example, an application for a cellular telephone platform maybe deployed on a large number of handheld devices. A tracing scenariomay have each device perform a tracer objective that may consume only alimited amount of resources. The cost-based analysis of tracerobjectives may ensure that the handheld devices may not be overwhelmedby the tracing workload.

The trace objectives may be evaluated for sampling rate and frequencyanalysis in block 507. The sampling rate and frequency analysis mayexamine data patterns to identify periodicities to identify whichperiodicities are dominant. The dominant periodicities may be used toadjust the sampling rate and data collection window to capture theperiodicities accurately. In some cases, a hypothesis of an initialsampling rate and data collection window may be tested by changing thesampling rate and data collection window to search for other dominantfrequencies in the data.

As the objectives are deployed in block 506 and data are collected, thedata may be analyzed in several different manners. For each tracerobjective, an input stream may be collected along with measured results.In block 510, the input stream may be culled to remove those inputparameters or values that have statistically small or insignificantcontributions to predicting the results. In block 512, other inputparameters may be added to a tracer objective. The process may iteratebetween blocks 506, 510, and 512 until the input parameters that arestatistically meaningful to predicting a measured result converge.

When examining a tracer objective to attempt to add input parameters inblock 512, related objects may be examined. The related objects may beobjects identified from static code analysis, such as from a controlflow graph or other relationship. In some cases, trace results that havesimilar periodicities may be examined to evaluate different parametersin an input stream.

The result of the iteration of blocks 506, 510, and 512 may result in amathematical model that may predict tracer results given a set of inputparameters. Each tracer objective may generate a separate mathematicalmodel.

The results may be analyzed for completeness in block 514. Acompleteness hypothesis may posit that the full range of inputconditions may have been experienced by the tracer objectives. Thehypothesis may be tested in block 514 by comparing the input streamsexperienced by different runs of the same trace objective, and in someembodiments, by comparing runs of different tracer objectives. When thehypothesis may not be validated, more data may be collected in block516.

When the completeness hypothesis may be validated in block 518, acombinability hypothesis may be tested in block 520. The combinabilityhypothesis may posit that two models created from different tracerobjectives may be combined into a larger model. The combinabilityhypothesis may be tested by joining two predictive models and testingthe results of the combined model using previously collected data or bytesting the results against real time data.

When the joined models do not yield a statistically meaningful result, anew tracer objective may be created in block 522 that combines the twotracer objectives. The resulting data collection and analysis may resultin a different model than the combined model initially tested for thecombinability hypothesis.

The combinability hypothesis may be tested for some or all of the tracerobjectives. When the hypothesis may be verified in block 524, thecollected data may be aggregated in block 526.

The aggregated data may be used in many different scenarios. In adebugging and testing scenario, the aggregated data may be used by adeveloper to understand program flow and to highlight any performancebottlenecks or other abnormalities that may be addressed. In anoptimization scenario, the aggregated data may be used by an automatedor semi-automated optimizer to apply different resources to certainportions of an application, for example.

FIG. 6 is a flowchart illustration of an embodiment 600 showing a methodfor creating and deploying trace objectives.

Other embodiments may use different sequencing, additional or fewersteps, and different nomenclature or terminology to accomplish similarfunctions. In some embodiments, various operations or set of operationsmay be performed in parallel with other operations, either in asynchronous or asynchronous manner. The steps selected here were chosento illustrate some principles of operations in a simplified form.

Embodiment 600 illustrates a method that creates tracer objectives byassigning various objects to tracer objectives. The tracer objectivesmay undergo a cost analysis that may cause the tracer objectives to bedivided into smaller tracer objectives, then the tracer objectives maybe dispatched.

Embodiment 600 illustrates a method that may be fully automated to beginan iterative method for tracing an application. The iterative method maycreate small, independent tracer objectives that may be deployed anditerated upon to converge on a set of statistically valid tracer modelsthat may reflect how the application performs. The method may beperformed on an arbitrary application and may automatically generate ameaningful understanding of an application without human intervention.In some embodiments, human intervention may be used at different stagesto influence or guide the automated discovery and analysis of anapplication.

In block 602, a list of objects to trace may be received. The list ofobjects may be identified through static code analysis or otherpreliminary analysis. An example of such analysis may be found in block303 of embodiment 300.

For each object in the list of objects in block 604, if the object iscontained in another tracer objective in block 606, the object may beskipped in block 608. When the object is not in a pre-existing tracerobjective in block 606, related objects may be identified in block 610.

Related objects may be any other objects to trace that may be suitablefor inclusion in a single tracer objective. For example, an object totrace may be a memory object. The memory object may be set by afunction, so the function may be added to the tracer objective. Otherfunctions may read the memory object, so those functions may be added aswell.

In the example, the function that may set the memory object may have astronger relationship to the memory object than the functions that mayread the memory object. Later in the process, objects with a weakerrelationship may be removed from the tracer objective when the tracerobjective may be too costly or burdensome to execute. Those objects thatmay be removed from a tracer objective may be added back to the list ofobjects.

For each related object in block 612, if the related object is alreadyin a pre-existing tracer objective in block 614, the object may beremoved in block 616.

The process of blocks 606 through 616 may be one method to gatherrelated objects into tracer objectives, but not duplicate efforts bytracing the same object in multiple tracer objectives. The example ofblocks 606 through 616 may assign objects to tracer objectives tomaximize coverage with a minimum number of tracer objectives.

With each object to be traced, a set of performance parameters may beidentified. In many cases, a template of tracer objectives may includemeasurable parameters that relate to a certain type of object. Forexample, a memory object may be traced by measuring the number ofchanges made, number of accesses, and other measurements. In anotherexample, a function or other block of executable code may be traced bymeasuring speed of completion, error flags thrown, heap allocation andusage, garbage collection frequency, number of instructions completedper unit time, percentage of time in active processing, percentage oftime in various waiting states, and other performance metrics. In yetanother example, a message interface may be traced by measuring thenumber of messages passed, payload of the messages, processing time andcommunication bandwidth allocated to each message, and other parameters.

Other embodiments may create tracer objectives that have overlappingcoverage, where a single object may be traced by two or more differenttracer objectives. Such embodiments may be useful when more resourcesmay be devoted to tracing.

After grouping the objects for a tracing objective in block 618, a setof default periodicity settings may be applied in block 620. A costanalysis may be performed in block 622. In some cases, two or moreobjectives may be created from a single tracer objective. An example ofsuch a method may be found later in this specification.

The tracer objective may be prepared for initial dispatch in block 624.Such preparation may define a communications configuration that maydefine how a tracer may communicate with a data gatherer. Thecommunication configuration may include an address for a data gatherer,as well as permissions, protocols, data schemas, or other information.

The tracer objectives may be dispatched in block 626 and resultscollected. The tracer objectives may be optimized in block 628 byremoving statistically insignificant input parameters and searching forpotentially significant input parameters.

After looping through blocks 626 and 628, the results may be aggregatedin block 630.

FIG. 7 is a flowchart illustration of an embodiment 700 showing a methodfor performing cost analysis on tracer objectives. Embodiment 700 mayillustrate one example of a process that may be performed in block 622of embodiment 600.

Other embodiments may use different sequencing, additional or fewersteps, and different nomenclature or terminology to accomplish similarfunctions. In some embodiments, various operations or set of operationsmay be performed in parallel with other operations, either in asynchronous or asynchronous manner. The steps selected here were chosento illustrate some principles of operations in a simplified form.

Embodiment 700 illustrates a method by which a tracer objective may beevaluated for cost impact and divided into smaller tracer objectives.The cost impact may be the resource consumption of a tracer objective.In some embodiments, the cost may be translated into a financial cost,while in other embodiments the cost may be in terms of resourcesconsumed by a tracer objective. Embodiment 700 is an example of thelatter type of cost analysis.

Embodiment 700 uses three different cost computations: performance cost,storage cost, and network bandwidth cost. Such an embodiment is anexample of a cost analysis that may have multiple, independent costfunctions to satisfy. Other embodiments may have more or fewer costfunctions to evaluate.

An objective may be received in block 702.

In some embodiments, a test run may be performed using the tracerobjective in block 704. In such embodiments, the performance of a tracermay be measured to estimate the cost components. In other embodiments, astatic code analysis may be performed of the tracer objective todetermine the various cost components.

An estimate of the computational cost may be performed in block 706. Anestimate of the storage cost may be performed in block 708, and anestimate of the network bandwidth cost may be performed in block 710.The overall cost of the tracer objective may be determined in block 712.

Computational cost or processor cost may reflect the amount of processorresources that may be incurred when executing a tracer objective. Inmany cases, a tracing operation may be substantially more complex than asimple operation of an application. For example, some tracers may incur10 or more processor steps to analyze a single processor action in anapplication.

Storage costs may reflect the amount of nonvolatile or volatile memorythat may be consumed by a tracer objective. In many cases, a tracerobjective may collect a large amount of data that may be stored andprocessed. The storage costs for a tracer objective may be very large insome cases, which may limit performance.

Network bandwidth costs may be the resources consumed in transmittingcollected data to a data repository. The network resources may includeoperations of a network interface card, network connection, and othernetwork related resources. As larger amounts of data may be moved acrossa network connection, a network connection may become saturated andcause disruption to other communications.

When the cost is above a predefined threshold in block 714, theobjective may be divided into two or more smaller tracer objectives inblock 716. An example of such a process may be illustrated in anotherembodiment described later in this specification.

When the cost is below the predefined threshold in block 714, a datacollection mechanism may be configured for the tracer objective in block718 and the tracer objective may be sent to a dispatcher in block 720.

The data collection mechanism of block 718 may define how the data maybe collected. In some embodiments, the data collection mechanism mayinclude a destination device description that may collect data, as wellas any communication parameters or settings.

FIG. 8 is a flowchart illustration of an embodiment 800 showing a methodfor dividing tracer objectives into smaller tracer objectives.Embodiment 800 may illustrate one example of a process that may beperformed in block 716 of embodiment 700.

Other embodiments may use different sequencing, additional or fewersteps, and different nomenclature or terminology to accomplish similarfunctions. In some embodiments, various operations or set of operationsmay be performed in parallel with other operations, either in asynchronous or asynchronous manner. The steps selected here were chosento illustrate some principles of operations in a simplified form.

Embodiment 800 illustrates one method by which a tracer objective may betrimmed to meet a cost objective. Embodiment 800 illustrates merely onemethod by which a tracer objective may be made smaller using anautomated process. In embodiment 800, objects may be sorted based on astrength of relationship, then objects with stronger relationships maybe consolidated into a tracer objectives. Any remaining objects may berecycled into a new tracer objective.

A tracer objective may be received in block 802.

For each object in the tracer objective in block 804, a costcontribution of the object may be estimated in block 806. The costcontribution may be the cost of tracing that object.

Relationships of the object to other objects within the trace objectivemay be identified in block 808 and the relationships may be scored inblock 810. The scoring may reflect a strength of a relationship.

A new objective may be started in block 812 with a starting object inblock 814. Relationships between the object and other objects may besorted by score in block 816. The sorting may result in the strongestrelationships being analyzed first.

A relationship may be selected in block 818 and tentatively added to thetracer objective. The cost of the tracer objective may be estimated inblock 820. The cost estimation in block 820 may utilize the costcontribution determined in block 806. If the cost is below a thresholdin block 822, the process may return to block 818 to add another objectto the tracer objective.

When the cost is above the threshold in block 822, the last object maybe removed from the tracer objective. In such a situation, adding thelast object may have made the trace objective go over the costallocation, and therefore it may be removed.

When more objects are still available but have not been placed in atracer objective in block 826, the process may return to block 812 tostart a new tracer objective. When all objects have been processed inblock 826, the tracer objectives may be deployed in block 828.

FIG. 9 is a diagram illustration of an embodiment 900 illustrating aprocess for tuning the sampling rate and data collection window for atracer objective.

Embodiment 900 illustrates an example process where periodicity analysismay be used to refine a tracer objective's data collection. In someembodiments, each tracer objective may be executed using defaultsampling rates and data collection windows, then these parameters may berefined after looking at the actual data collected.

In block 902, a periodicity may be assumed for a tracer objective. Theperiodicity may be a default periodicity that may be derived from aninitial analysis of an application. In many cases, the defaultperiodicity may reflect periodic behavior of an application as a whole,whereas a tracer objective may generate data with a different set ofperiodic behavior. However, a first run of a tracer objective may beperformed with the default periodicity as a starting point.

The first results of a tracer objective may be analyzed in block 904 byusing autocorrelation in block 906, which may generate characteristicperiodicities or frequencies in the data. From such analysis, dominantupper and lower frequencies may be identified in block 908.

A dominant upper frequency or shortest periodicity may be used to set asampling rate. In many cases, a sampling rate may be set so that 5, 10,20, or more samples may be taken within a single period of the dominantupper frequency.

Similarly, a dominant lower frequency or longest periodicity may be usedto set a data collection window. In many cases, a data collection windowmay be set to capture at least 2, 3, 4, 5, or more instances of thelongest periodicity.

After analyzing the initial run of a tracer objective, the tracerobjective may be updated in block 910 and dispatched in block 912.

FIG. 10 is a flowchart illustration of an embodiment 1000 showing amethod with a feedback look for evaluating tracer objective results.Embodiment 1000 may illustrate one example of a process that may beperformed in blocks 626 and 628 of embodiment 600.

Other embodiments may use different sequencing, additional or fewersteps, and different nomenclature or terminology to accomplish similarfunctions. In some embodiments, various operations or set of operationsmay be performed in parallel with other operations, either in asynchronous or asynchronous manner. The steps selected here were chosento illustrate some principles of operations in a simplified form.

Embodiment 1000 illustrates an embodiment where the input parameters fora tracer objective may be evaluated and iterated upon to converge on aset of statistically meaningful input parameters. Embodiment 1000 maydiscard those input parameters that may have little statisticalrelationship to a measured parameter and may attempt to add new inputparameters that may have a relationship to the measured object.

A results set may be received for a tracer objective in block 1002, anda profile model may be constructed of the results in block 1004. Theprofile model may be a mathematical expression of the relationshipbetween the input stream and the measured results. The profile model maybe created using linear or nonlinear regression, curve fitting, or anyof many different techniques for expressing a set of observations. Inmany cases, the profile model may have correlation factors or otherfactors that may indicate the degree or importance of an input factor tothe profile model.

The input parameters may be sorted by importance in block 1006. Thefirst input parameter may be selected in block 1008. Other tracerobjectives with the same input parameter may be identified in block1010.

For each of the objectives identified in block 1010, the objectives maybe analyzed in block 1012. The relevant input parameters may beidentified in block 1014. The relevant input parameters may be any ofthe parameters for that tracer objective where there may be a minimum ofstatistical correlation to the measured parameter.

For each of the parameters in block 1016, if the parameter is in thecurrent tracer objective, or was previously considered in the currenttracer objective, the parameter may be skipped in block 1020.

If the parameter has not been examined in the current tracer objectivein block 1018, the input parameter may be added to the input list inblock 1022. A relevancy score may be calculated in block 1024 for theparameter.

The relevancy score may indicate the expected degree to which theparameter may be relevant to the current tracer objective. In someembodiments, the relevancy score may be a factor of the strength ofrelationship between the current tracer objective and the related tracerobjective being examined, along with the relative importance of theinput parameter to the related tracer objective.

After processing all of the parameters in block 1016 for each of theobjectives in block 1012, if another relevant input parameter may beprocessed in block 1026, the process may return to block 1008 to addstill more candidate input parameters.

In block 1028, non-relevant input parameters within the current tracerobjective may be removed.

The list of potential input parameters may be sorted by score in block1030. The list may include all of the parameters added in block 1022.

The top group of input parameters may be selected in block 1032. The topgroup may contain input parameters with a score above a given threshold.Provided that the group is not an empty set in block 1034, the group maybe added to the tracer objective in block 1036 and dispatched forprocessing again in block 1038. The results of the trace objective maybe used as input to block 1002.

When the set of available input parameters is an empty set in block1034, the iteration may end in block 1040 as all of the potential inputparameters may have been exhausted.

FIG. 11 is a flowchart illustration of an embodiment 1100 showing amethod for iterating on tracer objectives using frequency similarities.Embodiment 1000 may illustrate another example of a process that may beperformed in blocks 626 and 628 of embodiment 600.

Other embodiments may use different sequencing, additional or fewersteps, and different nomenclature or terminology to accomplish similarfunctions. In some embodiments, various operations or set of operationsmay be performed in parallel with other operations, either in asynchronous or asynchronous manner. The steps selected here were chosento illustrate some principles of operations in a simplified form.

Embodiment 1100 may be similar to embodiment 1000 in that a tracerobjective may be updated with input parameters that may have alikelihood of being statistically significant. Embodiment 1100 maygather those input parameters from periodicity analysis of varioustracer objectives. Those tracer objectives with similar frequencysignatures or periodicities may be candidates for having statisticallyrelevant input parameters.

In block 1102, results from many tracer objectives may be received. Foreach objective in block 1104, a periodicity analysis may be performed inblock 1106 to identify frequencies or periods within the data. Afrequency profile or signature may be created in block 1108.

The frequency profile may include multiple frequencies and the intensityor strength of the various frequencies. The frequency profile may beused as a signature to represent the behavior of the data collected bythe tracer objectives.

A tracer objective may be selected in block 1112 as a startingobjective. In embodiment 1100, each tracer objective may be evaluated toattempt to find additional input parameters that may be related to agiven traced object or observed data point. The process may iterate toadd potential new input parameters, test the new parameters, anditerate.

In many embodiments, each iteration may include removing those inputparameters that may be statistically insignificant while attempting toadd input parameters that may be statistically significant.

For each tracer objective in block 1114, a similarity score may bedetermined by matching the frequency signatures of the objectiveselected in block 1112 with the tracer objectives analyzed in block1114. The similarity score may be a statistical measurement of thecorrelation or similarity of the two frequency signatures.

The tracer objectives may be sorted by similarity score in block 1118.Starting with the most similar frequency signature in block 1120, eachinput parameter may be analyzed in block 1122 to determine a relevancescore. The relevance score may take into account the similarity of thefrequency signatures coupled with the relevance of the input parameterto the data collected in the tracer objective selected in block 1120. Inmany embodiments, a similarity score created in block 1116 may bemultiplied with an influence factor for the input parameter to yield arelevance score.

The scored input parameters may be sorted by score in block 1126. Aparameter may be selected in block 1128 and, when the parameter may beabove a threshold in block 1130, the parameter may be added to thetracer objective and the process may loop back to 1128 to select thenext parameter in the sorted list.

When a parameter does not meet the relevance threshold in block 1130 butsome new parameters may have been added in block 1134 and additionalobjectives remain to be processed in block 1140, the process may returnto block 1120 to attempt to add more input parameters from other tracerobjectives.

When a parameter does not meet the relevance threshold in block 1130 andno new parameters have been added in block 1134, the iterating on theobjective may be stopped in block 1138. At this stage, the process ofembodiment 1100 may have not identified any new input parameters thatmay potentially be relevant.

After processing each objective in block 1140 to generate inputparameters, when additional objectives have not undergone inputparameter analysis in block 1142, the process may return to block 1112to select another tracer objective for analysis.

After each tracer objective has been analyzed for additional inputparameters in block 1142 and at least some of the tracer objectives mayhave been updated in block 1144, the updated objectives may bedispatched in block 1146. When no updated objectives may be available inblock 1144, the iteration process may halt in block 1148.

FIG. 12 is a diagram illustration of an embodiment 1200 showing a methodfor validating profile models. Embodiment 1200 illustrates a methodwhereby profile models may be generated using test objectives, which maybe run on complex, highly instrumented devices. The models may then bevalidated by lighter weight monitoring systems that may be deployed onproduction systems.

In one use model, an application may be evaluated using a highlyinstrumented test environment using independent trace objectives thatmay capture detailed data. From the data, profile models of smallelements of the application may be created. In order to test the profilemodels, the models may be deployed on production hardware that may ormay not have the capabilities to perform detailed data collection.

In an example, a mobile telephone application may be tested using avirtualized version of a mobile telephone, where the virtualized versionmay execute on a desktop computer with large amounts of computationalpower. The data collection may be performed using trace objectives thatmay be executed along with the application under test. Once a profilemodel has been generated that may represent the data, the model may bedispatched to a production mobile phone device that may perform a verylightweight monitoring that merely tests one small profile model.Because the profile model may not consume many resources, a monitor maycollect data on the mobile phone to generate an error statistic.

In block 1202, trace objectives may be created, and those objectives maybe deployed in block 1204. Profile models may be generated from theresulting data in block 1206.

The profile models may be deployed to devices in block 1208, where thedevices in block 1208 may have monitoring agents installed.

The profile models may have one or more input parameters and may performa mathematical function, then return a predicted result. The monitoringagents may capture input parameters from actual usage, perform thecalculations defined in the model, the compare the predictive result tothe actual result. The monitoring agent may generate an error statisticthat may be derived from the difference between a predictive result andan actual result.

Those models with high error statistics in block 1210 may update a traceobjective in block 1212 and re-submit the trace objective in block 1204.Those models with low error statistics in block 1214 may be assumed tobe accurate models and the monitoring frequency may be lowered orremoved in block 1216. The models may be aggregated with other models inblock 1218.

The monitors and profile models may be deployed as a general purposemonitoring system that may detect when performance, input data, or otherconditions may have gone awry. In such embodiments, the profile modelsmay be created to monitor variables or conditions that may causesubstantial harm or otherwise warn of adverse conditions. Such modelsmay be derived from the aggregated data in some cases.

FIG. 13 is a flowchart illustration of an embodiment 1300 showing amethod for analyzing results from trace objectives.

Other embodiments may use different sequencing, additional or fewersteps, and different nomenclature or terminology to accomplish similarfunctions. In some embodiments, various operations or set of operationsmay be performed in parallel with other operations, either in asynchronous or asynchronous manner. The steps selected here were chosento illustrate some principles of operations in a simplified form.

Embodiment 1300 illustrates merely one example of a method for analyzingtrace objective results. Embodiment 1300 illustrates an example analysismethod that compares multiple trace objective results from separateinstances of a trace objective. In many cases, a single trace objectivemay be executed multiple times, either on multiple devices a varioustimes or on the same device but at different times. The results sets maybe analyzed to determine whether or not the results may be consistentand predictable. Consistent and predictable results may be consideredgood results that may be aggregated with other similarly good results.

Embodiment 1300 is an example of an embodiment that may analyze theinput stream and results stream separately to make decisions using eachstream.

Each set of results may be processed in block 1302. For each set ofresults in block 1302, summary statistics may be generated for the inputstream in block 1304 and the input stream may be characterized andclassified in block 1306. Similarly, the results stream may have summarystatistics generated in block 1308 and characterizations andclassifications performed in block 1310. A profile model of the resultsmay be created in block 1312.

The statistics generated in blocks 1304 and 1308 may be high levelrepresentations of the data. Such statistics may include averages,medians, standard deviations, and other descriptors. Thecharacterizations and classifications performed in blocks 1306 and 1310may involve curve fitting, statistical comparisons to standard curves,linear and nonlinear regression analysis, or other classifications.

The profile model generated in block 1312 may be any type ofmathematical or other expression of the behavior of the observed data.The profile model may have input parameters that may be drawn from theinput stream to predict the values of the results stream.

An objective may be selected in block 1314. All of the results set forthe objective may be identified in block 1316. In some embodiments, manyresults sets may be generated, but the operations of embodiment 1300 mayassume at least two results sets may be present for the purposes ofillustration.

The profile model of each instance may be compared in block 1318. Whenthe profile model of the instances is the same in block 1320, the modelmay be selected to represent the observed data. In many embodiments, thecomparison of numerical values generated during profile model generationmay not be exact. In such embodiments, the comparison of profile modelsin block 1318 may consider models similar using a statistical confidencefactor, such as 0.99 or greater for example.

When the profile models are not the same in block 1320, the inputstreams may be compared in block 1324. When the input streams are notsimilar in block 1326, the objective may be re-executed in block 1328with longer runtime.

When the input streams are not similar, one or both of the objectivesmay not have experienced the full range of input variations. As such,any model generated from the input streams may not fully represent theactual behavior of the application. Such a condition may occur when thedata gathering window does not fully encompass at least a small numberof periods, for example, where the periods may be statisticallysignificant parameters in a profile model.

When the input streams are similar in block 1326, the profile model maybe missing parameters that may be statistically significant. In block1330, some parameters may be added to the trace objective. In someembodiments, statistically insignificant parameters may be removed fromthe trace objective in block 1332. The statistically insignificantparameters may be those parameters in a profile model with little or noeffect on the final result.

The updated trace objective may be resubmitted for scheduling anddeployment in block 1334.

If another objective can be processed in block 1336, the process mayreturn to block 1314 to select a new objective. When no more objectivesare available in block 1336, the results may be aggregated in block1338.

FIG. 14 is a diagram illustration of an embodiment 1400 showing anetwork environment with a tracing objective dispatcher. Embodiment 1400illustrates an environment with a dispatcher device 1402, tracinggenerator device 1404, and a set of tracer devices 1406, all of whichmay be connected by a network 1408.

Embodiment 1400 may illustrate a tracing dispatcher that may match atracing objective to a device that may execute the tracing objective.The match may be made based on the configuration of the tracing deviceand the estimated resource consumption of the tracing objective.

The dispatcher device 1402 may operate on a hardware platform 1410 andmay have a dispatcher 1412 that may dispatch various tracer objectives1414 to the tracer devices 1406. The dispatcher 1412 may consider thedevice configurations 1416 which may be collected and updated by atracing manager 1418.

The dispatcher 1412 may place tracer objectives on devices within atracer resource budget that may be defined for each device. The budgetmay identify a set of resources that may be set aside for tracingfunctions. As a tracing objective may be placed on a device, the tracerresource budget for the device may be updated, leaving an availableresource budget.

In many cases, the set of tracer devices 1406 may have differenthardware and software configurations, workloads, or other differencesthat may be taken into consideration when dispatching tracer objectives.A tracing manager 1418 may collect and update such device configurations1416 on an ongoing basis.

The dispatcher device 1402 may use tracer objectives 1414 that may havebeen created using a tracer generator device 1404. The tracer generatordevice 1404 may operate on a hardware platform 1420 and may have atracer objective generator 1422, which may create tracer objectives byanalyzing an application 1424.

The tracer devices 1406 may operate on a hardware platform 1426 and havea tracer 1428 that may execute a manifest of tracer objectives 1430against an instance of an application 1432.

FIG. 15 is a flowchart illustration of an embodiment 1500 showing amethod for deploying tracer objectives. Embodiment 1500 may illustrate ahigh level method, with a later embodiment illustrating some detailedexamples of how certain portions may be implemented.

Other embodiments may use different sequencing, additional or fewersteps, and different nomenclature or terminology to accomplish similarfunctions. In some embodiments, various operations or set of operationsmay be performed in parallel with other operations, either in asynchronous or asynchronous manner. The steps selected here were chosento illustrate some principles of operations in a simplified form.

Embodiment 1500 illustrates a high level process that characterizesdevices in block 1504, characterizes tracer objectives in block 1522,and deploys the objectives on the devices in block 1524. Embodiment 1500illustrates one method that may be used to dispatch tracer objectives,especially one in which the tracing devices may be differentlyconfigured.

A set of device descriptors may be received in block 1502. Thedescriptors may be network addresses or other identifiers for devicesthat may be deployed as tracer devices.

For each device in block 1506, many data points may be collected. In theexample of embodiment 1500, these data points may be illustrated asbeing collected prior to deploying tracer objectives. In manyembodiments, some of the various data points may change over time andmay be updated periodically. Other data points may be relativelyconstant and may not be updated as frequently.

A hardware configuration may be determined in block 1508. The hardwareconfiguration may include processing capabilities and capacities,storage capacities, and other hardware parameters.

A network topology may be determined in block 1510. The network topologymay include locating the tracing device within a network, which may beused as an input parameter when determining where to deploy a tracerobjective.

The software configuration of the tracer device may be determined inblock 1512. In some cases, the software configuration may includespecific tracing capabilities. Some embodiments may have anon-homogenous group of tracing devices, with some devices havingtracing capabilities that other devices may not have. Further, somedevices may have certain additional software components or workloadsthat may interfere, influence, or degrade tracing capabilities in somecases. Such knowledge may be useful in matching specific tracingobjectives to devices.

In some embodiments, a performance test may be performed in block 1514.The performance tests may measure certain performance capabilities thatmay be measured dynamically, as opposed to static analyses such asperformed in blocks 1508 through 1512.

The performance tests of block 1514 may measure processor capabilities,storage resources, network bandwidth, and other performance metrics. Insome cases, performance tests may be performed while the applicationunder test is executing. The performance tests may identify theresources consumed by the device, which may be used as a factor whencomputing a resource budget for tracing.

Predefined allocations may be identified in block 1516. The predefinedallocations may be any limitation or resource allocation that may takeprecedence over tracing. For example, a production application may beallocated to execute without any tracing during periods of highworkload. Such an allocation may be time based, as resources may beallocated based on a period of time. In another example, a device mayhave resources allocated to a second application or function that may beunrelated to the application under test and any associated tracingfunctions.

In some cases, certain devices may have allocated resources that may bededicated to tracing functions. For example, a device may have a storagesystem and network interface card that may be allocated to tracing,while another storage mechanism and network interface card may beallocated to the application under test. Such devices may be speciallyallocated for tracing, while other devices may have limited or noresource availability for tracing.

An initial tracer resource budget may be defined in block 1518. A tracerresource budget may define the resources that may be consumed by atracer objective for a particular device. In some cases, the tracerresource budget may be set as a percentage of overall capacity. Forexample, a tracer resource budget may be 5%, 10%, 20%, 25%, 50%, or someother percentage of resources.

In some cases, a tracer resource budget may be a percentage of availableresources. For example, the performance tests in block 1514 maydetermine that an application under test may consume 45% of theprocessor capacity, meaning that 55% of the processor capacity may benot be utilized and could be available for tracing. In a simplifiedversion of such an example, up to 55% of the processor resource could beallocated for tracing without adversely affecting the application.

After determining the various parameters, the configuration of thedevice may be stored. Some of the elements in the configuration may berelatively static, such as the hardware configuration and networktopology, while other elements such as the available resources maychange dramatically over time. Some embodiments may monitor theconfiguration and update various elements over time.

After characterizing the devices in block 1504, the tracer objectivesmay be characterized in block 1522. The deploying step of block 1524 maymatch the tracer objective characteristics with the devicecharacteristics and cause the tracer objectives to be executed. Theresults may be received and analyzed in block 1526.

FIG. 16 is a flowchart illustration of an embodiment 1600 showing amethod for tracer objective characterization and deployment.

Other embodiments may use different sequencing, additional or fewersteps, and different nomenclature or terminology to accomplish similarfunctions. In some embodiments, various operations or set of operationsmay be performed in parallel with other operations, either in asynchronous or asynchronous manner. The steps selected here were chosento illustrate some principles of operations in a simplified form.

Embodiment 1600 illustrates a detailed method for characterizing tracerobjectives then matching those tracer objectives with available devices.A manifest of tracer objectives may be created for each device, then themanifests may be deployed to the devices for execution.

The method of embodiment 1600 may attempt to place the most costlytracer objectives on the devices with the most available resources.Multiple tracer objectives may be added to a device until all of theallocated tracing resources may be utilized. Embodiment 1600 may attemptto use all of available tracing resources of each device being examined.Such an embodiment may result in some devices being fully loaded whileother devices may not have any tracer objectives.

The method of embodiment 1600 illustrates merely one method for matchingtracer objectives to devices, and other embodiments may have differentways for distributing tracer objectives. For example, another embodimentmay attempt to load all devices equally such that each device mayperform at least some tracing.

Device characterizations may be received in block 1602. An example ofdevice characterizations may be found in embodiment 1500.

The tracer objectives may be analyzed in block 1604 and then deployed inblock 1606.

The tracer objectives may be received in block 1608. For each tracerobjective in block 1610, an initial performance test may be performed inblock 1612. The costs associated with executing the tracer objective maybe estimated in block 1614 and stored in block 1616.

The costs for executing a tracer objective may be resource costs. Insome cases, several independent factors may make up the cost. Forexample, processors costs, storage costs, and network bandwidth costsmay be combined into the overall cost of executing a tracer objective.In embodiments where a dynamic performance test may not be performed inblock 1612, the costs may be estimated by static analysis of the tracerobjectives. A static analysis may estimate the processor load, storageusage, and network bandwidth usage for a given tracer objective.

The deployment of objectives may begin in block 1618 by sorting thedevices by available resources in block 1620. The trace objectives maybe sorted by estimated cost from most expensive to least costly in block1622.

A device may be selected in block 1624 and the next tracer objective maybe selected in block 1626. An evaluation may be made in block 1628 todetermine whether the objective may be deployed on the device. When thetracer objective can be deployed in block 1628, the tracer objective maybe added to the device's manifest in block 1630. When the tracerobjective cannot be deployed in block 1628, the objective may be skippedin block 1632.

The evaluation of block 1628 may evaluate the selected tracer objectivefor execution on the selected device. The evaluation may examine whetheror not any specific allocations may exist that may prevent the tracerobjective from being executed, as well as comparing the cost ofexecuting the tracer objective with the available resource budget on thedevice. Some embodiments may perform other tests or evaluations todetermine whether or not an objective may be placed on a device.

When more objectives are on the list in block 1634, the process mayreturn to block 1626. The loop back to block 1626 may process eachavailable tracer objective to attempt to use all of the availableresources on the selected device.

When all objectives have been processed in block 1634, if no tracerobjectives may have been placed in the manifest, the objectives may beevaluated in block 1638 for dividing into smaller tracer objectives. Theprocess may return to block 1608.

The operations of block 1638 may be reached when a device is selectedbut there are no tracer objectives that may be small enough or consumefewer resources than may be available on the device. In such asituation, the tracer objectives may be divided into two or more tracerobjectives and the placement may be retried.

In block 1638, a tracer objective may be evaluated for dividing into twoor more tracer objectives. In some cases, a tracer objective may bemodified by changing the sampling rate or setting other parameters sothat the cost impact may be lessened.

Provided that there are tracing objectives in the manifest in block1636, the available budget for the device may be updated in block 1640to reflect that the tracing objectives may be executing. The manifestmay be deployed in block 1642 to the selected device.

When more objectives and more devices still remain in block 1644, theprocess may return to block 1624 to process the next device. When moreobjectives remain but no more devices in block 1646, the process maywait in block 1648 until some of the tracer objectives to finishprocessing. At that point, remaining objectives may be allocated anddispatched. When all of the objectives have been allocated, the processmay end in block 1650, at which point an analysis operation may beperformed.

The foregoing description of the subject matter has been presented forpurposes of illustration and description. It is not intended to beexhaustive or to limit the subject matter to the precise form disclosed,and other modifications and variations may be possible in light of theabove teachings. The embodiment was chosen and described in order tobest explain the principles of the invention and its practicalapplication to thereby enable others skilled in the art to best utilizethe invention in various embodiments and various modifications as aresuited to the particular use contemplated. It is intended that theappended claims be construed to include other alternative embodimentsexcept insofar as limited by the prior art.

What is claimed is:
 1. A method performed by a computer processor, saidmethod comprising: receiving an application to instrument; performing apreliminary trace of said application while said application executes,and collecting preliminary results for one or more time series datasets; analyzing said preliminary results to identify a set of periodswithin said preliminary results; selecting a first period within saidset of periods; identifying a first trace objective for saidapplication, said first trace objective comprising a plurality of dataitems to collect and a first collection window comprising a size atleast as large as a longest frequency observed in the one or more timeseries data sets; and causing said first trace objective to be executedand collecting a first results set comprising said plurality of dataitems and a first input stream.
 2. The method of claim 1 furthercomprising: performing a first autocorrelation analysis of a first dataitem, said first data item being one of said plurality of data items,said first autocorrelation analysis being performed on said firstresults set; and identifying a first derived period for said first dataitem.
 3. The method of claim 2 further comprising: selecting a secondperiod within said set of periods; creating a second trace objective forsaid application, said second trace objective comprising said pluralityof data items to collect and a second collection window comprising atleast two times said second period; and causing said second traceobjective to be executed and collecting a second results set comprisingsaid plurality of data items and a second input stream.
 4. The method ofclaim 3 further comprising: performing a second autocorrelation analysisof said first data item, said second autocorrelation analysis beingperformed on said second results set; and identifying a second derivedperiod for said first data item.
 5. The method of claim 4 furthercomprising: comparing said first derived period and said second derivedperiod; and when said first derived period and said second derivedperiod are statistically different, creating a third trace objectivecomprising a third collection window comprising at least two times athird period, said third period being larger than said first period andsaid second period.
 6. The method of claim 5 further comprising: whensaid first derived period and said second derived period arestatistically similar, using said first derived period within a profilemodel for said first data item.
 7. The method of claim 6, said secondtrace objective being executed on a first processor during a first timeperiod, said third trace objective being executed on said firstprocessor at during a second time period.
 8. The method of claim 7, saidfirst time period overlapping said second time period.
 9. The method ofclaim 7, said first time period not overlapping said second time period.10. The method of claim 6, said second trace objective being executed ona first processor and said second trace objective being executed on asecond processor.
 11. The method of claim 10, said second traceobjective being executed during a first time period and said third traceobjective being executed during a second time period.
 12. The method ofclaim 11, said first time period overlapping said second time period.13. The method of claim 11, said first time period not overlapping saidsecond time period.
 14. The method of claim 11, said first processor andsaid second processor being comprised in a first device.
 15. The methodof claim 11, said first processor being comprised in a first device,said second processor being comprised in a second device.
 16. A systemcomprising: a processor; a trace objective generator executing on saidprocessor that: receives an application to instrument; performs apreliminary trace of said application while said application executes,and collects preliminary results for one or more time series data sets;analyzes said preliminary results to identify a set of periods withinsaid preliminary results; selects a first period within said set ofperiods; identifies a first trace objective for said application, saidfirst trace objective comprising a plurality of data items to collectand a first collection window comprising a size at least as large as alongest frequency observed in the one or more time series data sets; andcauses said first trace objective to be executed and collecting a firstresults set comprising said plurality of data items and a first inputstream.
 17. The system of claim 16, said trace objective generator thatfurther: performs a first autocorrelation analysis of a first data item,said first data item being one of said plurality of data items, saidfirst autocorrelation analysis being performed on said first resultsset; and identifies a first derived period for said first data item. 18.The system of claim 17, said trace objective generator that further:selects a second period within said set of periods; creates a secondtrace objective for said application, said second trace objectivecomprising said plurality of data items to collect and a secondcollection window; and causes said second trace objective to be executedand collecting a second results set comprising said plurality of dataitems and a second input stream.
 19. The system of claim 18, said traceobjective generator that further: performs a second autocorrelationanalysis of said first data item, said second autocorrelation analysisbeing performed on said second results set; and identifies a secondderived period for said first data item.
 20. The system of claim 19,said trace objective generator that further: compares said first derivedperiod and said second derived period; and when said first derivedperiod and said second derived period are statistically different,creates a third trace objective comprising a third collection window,said third period being larger than said first period and said secondperiod.